UDC 004.94=111 | DOI: https://doi.org/10.31617/zt.knute.2019(104)07 | |
KRYVORUCHKO Olena, E-mail: Ця електронна адреса захищена від спам-ботів. вам потрібно увімкнути JavaScript, щоб побачити її. ORCID: 0000-0002-7661-9227 |
DSc (Engineering), Professor, Head of Department of Software Engineering and Cyber Security of Kyiv National University of Trade and Economics 19, Kyoto str., Kyiv, 02156, Ukraine |
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KHOROLSKA Karyna, E-mail: Ця електронна адреса захищена від спам-ботів. вам потрібно увімкнути JavaScript, щоб побачити її. ORCID: 0000-0003-3270-4494 |
Server-side Developer, Softorino Inc. Huntington Beach, California, USA |
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CHUBAIEVSKYI Vitalii, E-mail: Ця електронна адреса захищена від спам-ботів. вам потрібно увімкнути JavaScript, щоб побачити її. ORCID:0000-0001-8078-2652 |
PhD (Political Sciences), Associate Professor of Department of Software Engineering and Cyber Security of Kyiv National University of Trade and Economics 19, Kyoto str., Kyiv, 02156, Ukraine |
USAGE OF NEURAL NETWORKS IN IMAGE RECOGNITION
This article focuses on the operation of the classification of blueprint parts. Classification characteristic is the main part of the designation of the part or product and their design documents, solving a number of topical tasks from creation of a single information language for automated systems to unification and standardization.
Keywords: neural network, object recognition, classification, domains.
REFERENCES
- Alexandre, L. A. (2016). 3D Object Recognition Using Convolutional Neural Networks with Transfer Learning Between Input Channels. In: Menegatti E., Michael N., Berns K., Yamaguchi H. (Eds). Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing. (vol. 302). Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-08338-4_64 [in English].
- Andre, Esteva, & Brett, Kuprel (2017).Dermatologist-level classification of skin cancer with deep neural networks. (Vol. 542), (pp. 115–118). 02 February. Retrieved from https://www.nature.com/articles/nature21056?TB_iframe=true&width=914.4&height=921.6. DOI: https://doi.org/10.1038/nature21056 [in English].
- Popescu, A. C., & Farid, H. (2005). Exposing digital forgeries by detecting traces of resampling. IEEE Transactions on signal processing. (Vol. 53), 2, (pp. 758-767). DOI: https://doi.org/10.1109/TSP.2004.839932 [in English].
- Qian, Y., Dong, J., Wang, W., & Tan, T. (2015). Deep learning for steganalysis via convolutional neural networks. Media Watermarking, Security and Forensics. (Vol. 9409), (pp. 94 090J). DOI: https://doi.org/10.1117/12.2083479 [in English].
- Lin, M., Chen, Q., & Yan, S. (2014). Network in network, in International Conference on Learning Representations [in English].
- Ciresan, D. C., Meier, U. J., Masci, Gambardella L. M., & Schmidhuber J. (2011). High-performance neural networks for visual object classification. Arxiv preprint arXiv:1102.0183 [in English].
- Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks, in Advances in neural information processing systems, (pp. 1097-1105) [in English].